一种面向航空图像的自适应目标计数模型

Adaptive Object Counting Model for Aerial Imagery

目标计数旨在获取给定图像中包含的车辆、建筑物、人物等特定种类目标的数量,对城市规划、应急响应、国家安全等具有重要意义。当前目标计数任务主要依赖于低空摄像头所拍摄的图像,存在目标易被遮挡和计数空间范围小等突出问题。高清航空遥感图像的广泛使用使大范围目标计数成为可能。然而,面向航空图像的目标计数任务存在目标尺度差异大、分布密集、方向不确定等挑战,现有基于低空图像的目标检测计数模型和回归计数模型均无法适用于航空图像的目标计数。针对该问题,提出了一种面向航空图像的自适应目标计数模型。首先,利用几何自适应高斯卷积方法解决目标尺度变化问题;然后,利用基于结构相似性的图片损失判断方法解决目标密集区域计数稳定性较差的问题。实验结果表明,所提模型相较于基准模型取得了更好的目标计数精度。

Object counting aims to obtain the number of specific types of objects such as vehicles,buildings,people contained in a given image,which is of great significance to urban planning,emergency response,national security,etc.The current object coun-ting task mainly relies on the images taken by low-altitude cameras,and there are obvious problems such as the object being easily occluded and the small counting space range.Widespread use of high-definition aerial remote sensing imagery makes it possible to count objects in large areas.However,the object counting task for aerial images has challenges such as large differences in object scales,dense distribution,and uncertain orientation.Existing object detection counting models and regression counting models based on low-altitude images are not suitable for object counting in aerial images.To solve this problem,this paper proposes an adaptive object counting model for aerial images.Firstly,the geometric adaptive Gaussian convolution method is used to solve the problem of object scale variation.Then,the image loss judgment method based on structural similarity is used to solve the pro-blem of poor counting stability of object dense regions.Experimental analysis shows that the proposed model can achieve better object count accuracy than the benchmark model.